Local-Scale Valley Wind Retrieval Using an Artificial Neural Network Applied to Routine Weather Observations

Florian Dupuy Laboratoire d’Aérologie, Université de Toulouse, CNRS, UPS, Toulouse, and CEA, DEN, Cadarache, Laboratoire de Modélisation des Transferts dans l’Environnement, Saint-Paul-lès-Durance, France

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Gert-Jan Duine Earth Research Institute, University of California, Santa Barbara, Santa Barbara, California

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Pierre Durand Laboratoire d’Aérologie, Université de Toulouse, CNRS, UPS, Toulouse, France

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Thierry Hedde CEA, DEN, Cadarache, Laboratoire de Modélisation des Transferts dans l’Environnement, Saint-Paul-lès-Durance, France

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Pierre Roubin CEA, DEN, Cadarache, Laboratoire de Modélisation des Transferts dans l’Environnement, Saint-Paul-lès-Durance, France

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Eric Pardyjak Department of Mechanical Engineering, University of Utah, Salt Lake City, Utah

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Abstract

We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting method, based solely on a vertical temperature difference, as well as a multilinear regression model. The assessment of the wind speed and direction indicates good performance (i.e., wind speed bias of −0.28 m s−1 and 84% of calculated directions stray from observations by less than 45°). Major sources of error are due to the misrepresentation of cross-valley winds and very light winds. The validated method was then successfully applied to a 1-yr period with a similar performance. Potentially, this method could be used to downscale valley wind characteristics for unresolved valleys in mesoscale simulations.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Thierry Hedde, thierry.hedde@cea.fr

Abstract

We hereby present a new method with which to nowcast a thermally driven, downvalley wind using an artificial neural network (ANN) based on remote observations. The method allows the retrieval of wind speed and direction. The ANN was trained and evaluated using a 3-month winter-period dataset of routine weather observations made in and above the valley. The targeted valley winds feature two main directions (91% of the total dataset) that are aligned with the valley axis. They result from downward momentum transport, channeling mechanisms, and thermally driven flows. A selection procedure of the most pertinent ANN input variables, among the routine observations, highlighted three key variables: a potential temperature difference between the top and the bottom of the valley and the two wind components above the valley. These variables are directly related to the mechanisms that generate the valley winds. The performance of the ANN method improves on an earlier-proposed nowcasting method, based solely on a vertical temperature difference, as well as a multilinear regression model. The assessment of the wind speed and direction indicates good performance (i.e., wind speed bias of −0.28 m s−1 and 84% of calculated directions stray from observations by less than 45°). Major sources of error are due to the misrepresentation of cross-valley winds and very light winds. The validated method was then successfully applied to a 1-yr period with a similar performance. Potentially, this method could be used to downscale valley wind characteristics for unresolved valleys in mesoscale simulations.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Thierry Hedde, thierry.hedde@cea.fr
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  • Bastin, S., P. Drobinski, A. Dabas, P. Delville, O. Reitebuch, and C. Werner, 2005: Impact of the Rhône and Durance valleys on sea-breeze circulation in the Marseille area. Atmos. Res., 74, 303328, https://doi.org/10.1016/j.atmosres.2004.04.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beale, M., M. T. Hagan, and H. B. Demuth, 2010: Neural network toolbox 7: User’s guide. MathWorks, 951 pp.

  • Berthou, S., and Coauthors, 2016: Influence of submonthly air–sea coupling on heavy precipitation events in the western Mediterranean basin. Quart. J. Roy. Meteor. Soc., 142, 453471, https://doi.org/10.1002/qj.2717.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burns, P., and C. Chemel, 2015: Interactions between downslope flows and a developing cold-air pool. Bound.-Layer Meteor., 154, 5780, https://doi.org/10.1007/s10546-014-9958-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cadenas, E., and W. Rivera, 2009: Short term wind speed forecasting in la Venta, Oaxaca, México, using artificial neural networks. Renewable Energy, 34, 274278, https://doi.org/10.1016/j.renene.2008.03.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chow, F. K., S. F. De Wekker, and B. J. Snyder, 2012: Mountain Weather Research and Forecasting: Recent Progress and Current Challenges. Springer Science & Business Media, 750 pp.

    • Crossref
    • Export Citation
  • Clements, C. B., C. D. Whiteman, and J. D. Horel, 2003: Cold-air-pool structure and evolution in a mountain basin: Peter Sinks, Utah. J. Appl. Meteor., 42, 752768, https://doi.org/10.1175/1520-0450(2003)042<0752:CSAEIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clements, W. E., J. A. Archuleta, and P. H. Gudiksen, 1989: Experimental design of the 1984 ascot field study. J. Appl. Meteor., 28, 405413, https://doi.org/10.1175/1520-0450(1989)028<0405:EDOTAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delon, C., D. Serça, C. Boissard, R. Dupont, A. Dutot, P. Laville, P. De Rosnay, and R. Delmas, 2007: Soil no emissions modelling using artificial neural network. Tellus, 59B, 502513, https://doi.org/10.1111/j.1600-0889.2007.00254.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doran, J., J. D. Fast, and J. Horel, 2002: The VTMX 2000 campaign. Bull. Amer. Meteor. Soc., 83, 537551, https://doi.org/10.1175/1520-0477(2002)083<0537:TVC>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dreyfus, G., J. Martinez, M. Samuelides, M. B. Gordon, F. Badran, S. Thiria, and L. Herault, 2002: Réseaux de Neurones: Méthodologie et Applications. Eyrolles, 408 pp.

  • Duine, G.-J., T. Hedde, P. Roubin, and P. Durand, 2016: A simple method based on routine observations to nowcast down-valley flows in shallow, narrow valleys. J. Appl. Meteor. Climatol., 55, 14971511, https://doi.org/10.1175/JAMC-D-15-0274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duine, G.-J., T. Hedde, P. Roubin, P. Durand, M. Lothon, F. Lohou, P. Augustin, and M. Fourmentin, 2017: Characterization of valley flows within two confluent valleys under stable conditions: Observations from the KASCADE field experiment. Quart. J. Roy. Meteor. Soc., 143, 18861902, https://doi.org/10.1002/qj.3049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernando, H., and Coauthors, 2015: The Materhorn: Unraveling the intricacies of mountain weather. Bull. Amer. Meteor. Soc., 96, 19451967, https://doi.org/10.1175/BAMS-D-13-00131.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gardner, M. W., and S. Dorling, 1998: Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmos. Environ., 32, 26272636, https://doi.org/10.1016/S1352-2310(97)00447-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khosravi, A., R. Koury, L. Machado, and J. Pabon, 2018: Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustainable Energy Technol. Assess., 25, 146160, https://doi.org/10.1016/j.seta.2018.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lareau, N. P., E. Crosman, C. D. Whiteman, J. D. Horel, S. W. Hoch, W. O. Brown, and T. W. Horst, 2013: The persistent cold-air pool study. Bull. Amer. Meteor. Soc., 94, 5163, https://doi.org/10.1175/BAMS-D-11-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • More, A., and M. Deo, 2003: Forecasting wind with neural networks. Mar. Structures, 16, 3549, https://doi.org/10.1016/S0951-8339(02)00053-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, R. C., M. J. Falvey, M. Araya, and M. Jacques-Coper, 2013: Strong down-valley low-level jets over the Atacama Desert: Observational characterization. J. Appl. Meteor. Climatol., 52, 27352752, https://doi.org/10.1175/JAMC-D-13-063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Philippopoulos, K., and D. Deligiorgi, 2012: Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography. Renewable Energy, 38, 7582, https://doi.org/10.1016/j.renene.2011.07.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, J., and Coauthors, 2011: COLPEX: Field and numerical studies over a region of small hills. Bull. Amer. Meteor. Soc., 92, 16361650, https://doi.org/10.1175/2011BAMS3032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotach, M. W., and Coauthors, 2017: Investigating exchange processes over complex topography: The Innsbruck Box (i-Box). Bull. Amer. Meteor. Soc., 98, 787805, https://doi.org/10.1175/BAMS-D-15-00246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sabatier, T., A. Paci, G. Canut, Y. Largeron, A. Dabas, J.-M. Donier, and T. Douffet, 2018: Wintertime local wind dynamics from scanning Doppler lidar and air quality in the Arve River valley. Atmosphere, 9, 118, https://doi.org/10.3390/atmos9040118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santos-Alamillos, F., D. Pozo-Vázquez, J. Ruiz-Arias, V. Lara-Fanego, and J. Tovar-Pescador, 2013: Analysis of WRF Model wind estimate sensitivity to physics parameterization choice and terrain representation in Andalusia (southern Spain). J. Appl. Meteor. Climatol., 52, 15921609, https://doi.org/10.1175/JAMC-D-12-0204.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simpson, J. E., 1994: Sea Breeze and Local Winds. Cambridge University Press, 252 pp.

  • Whiteman, C. D., and J. C. Doran, 1993: The relationship between overlying synoptic-scale flows and winds within a valley. J. Appl. Meteor., 32, 16691682, https://doi.org/10.1175/1520-0450(1993)032<1669:TRBOSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whiteman, C. D., and Coauthors, 2008: METCRAX 2006: Meteorological experiments in Arizona’s meteor crater. Bull. Amer. Meteor. Soc., 89, 16651680, https://doi.org/10.1175/2008BAMS2574.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yegnanarayana, B., 2009: Artificial Neural Networks. PHI Learning Pvt. Ltd., 476 pp.

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